{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "\n",
    "import preprocessing as pp\n",
    "import model_gen as vae\n",
    "\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_step = 24\n",
    "\n",
    "data_path = '/Users/baconbaker/Documents/Studium/ANM/anm-project/data/test_data/'\n",
    "model_dir = 'deploy/'\n",
    "thresh_dir = 'deploy/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_anom(df):\n",
    "    problems = []\n",
    "\n",
    "    thresh = pd.read_csv(thresh_dir+'thresh.csv')\n",
    "\n",
    "    for name in ['num', 'avg_time']:\n",
    "        x_test = pp.get_esb_train_data(df, name, time_step)\n",
    "\n",
    "        model = keras.models.load_model(model_dir+name)\n",
    "        x_test_pred = model.predict(x_test)\n",
    "        test_mae_loss = np.mean(np.abs(x_test_pred - x_test), axis=1)\n",
    "        test_mae_loss = test_mae_loss.reshape((-1))\n",
    "        anomalies = np.greater(test_mae_loss, thresh[thresh.name==name]['thresh'].values[0])\n",
    "        \n",
    "        plt.hist(test_mae_loss, bins=50)\n",
    "        plt.xlabel(\"test MAE loss\")\n",
    "        plt.ylabel(\"No of samples\")\n",
    "        plt.show()\n",
    "        \n",
    "        plt.plot(x_test[0])\n",
    "        plt.plot(model.predict(x_test)[0])\n",
    "        plt.show()\n",
    "        if True in anomalies:\n",
    "            indices = []\n",
    "            for i in range(len(anomalies)):\n",
    "                if anomalies[i]:\n",
    "                    indices.append(i)\n",
    "                    \n",
    "            print(\"Anomaly in \", name)\n",
    "            problems.append((name, indices))\n",
    "        if problems == []:\n",
    "            print(\"No anomalies found in \", name)\n",
    "    return problems"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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aGINfkgbGrh5pIPa0bpJRWKorcfs+d6O+YngsLf4kr0/y90nuSLJ+HDVI0lCNPPiT7AX8AfCvgGOAM5McM+o6JGmoxtHifwlwR1XdWVXfBy4BTh1DHZI0SOPo4z8c+OaU5buBn5i+UZJ1wLp28dEkfz+C2sbtEODb4y5iD+B5aHgeGkt+HnLBUu6tv+NO234x5+GHZ1q5x365W1UbgY3jrmOUkmytqslx1zFunoeG56HheWgs5XkYR1fPPcBzpywf0a6TJI3AOIL/C8DRSY5M8nTgDGDzGOqQpEEaeVdPVe1M8m7gb4G9gI9W1VdGXccealBdW3PwPDQ8Dw3PQ2PJzkOqaqn2JUlaBpyyQZIGxuCXpIEx+Mdgvikrkrw6yU1JdiZ5yzhqHIUO5+H8JLcl+VKSLUlmHJO83HU4Dz+f5MtJbknyuZV6pXvXqVySvDlJJVlxQzw7/C78XJId7e/CLUn+zaIOVFU+Rvig+UL768DzgKcDXwSOmbbNBPAi4I+Bt4y75jGehxOB/drnvwB8Ytx1j+k8PHPK81OAq8Zd9zjOQ7vdgcB1wOeByXHXPYbfhZ8Dfn93j2WLf/TmnbKiqrZV1ZeAH4yjwBHpch6uqarH2sXP01zzsdJ0OQ/fmbK4P7ASR2R0ncrlN4ALgH8cZXEjMrLpbAz+0ZtpyorDx1TLOC30PJwNfLrXisaj03lI8q4kXwd+EzhnRLWN0rznIckJwHOraqXOL93138Sb2+7Py5I8d4bX52Xwa4+X5CxgErhw3LWMS1X9QVU9H/gl4P3jrmfUkjwN+C3gPeOuZcz+CpioqhcBVwObFrMTg3/0nLKi0ek8JPmXwC8Dp1TV90ZU2ygt9PfhEuC0Pgsak/nOw4HAC4Frk2wDXgpsXmFf8M77u1BVD0z5d/AR4MWLOZDBP3pOWdGY9zwkOR74ME3obx9DjaPQ5TwcPWXxZOBrI6xvVOY8D1X1cFUdUlUTVTVB853PKVW1dTzl9qLL78KhUxZPAW5fzIH22Nk5V6qaZcqKJP8Z2FpVm5P8C+By4NnAv07y61X1Y2Mse8l1OQ80XTsHAH+RBOAbVXXK2IruQcfz8O72L59/Ah4E1o6v4n50PA8rWsdzcE6SU4CdwD/QjPJZMKdskKSBsatHkgbG4JekgTH4JWlgDH5JGhiDX5IGxuDXspXkoCS/uBvvPy/JfrO8dm2Sb6QdR9qu+8skj86wj39M8qwp616T5OEpMyje0g7HnH6MbUkOWWz90mIZ/FrODgIWHfzAecCMwd96CHgFNB8ywKEzbHMmzYU3b5q2/vqqOm7K43/sRp3SkjL4tZxtAJ7ftqgvBEjy3iRfaCex+vV23f5JrkzyxSS3JvmZJOcAhwHXJLlmlv1fQnP1JDTB/qmpLyZ5Ps0FZu+n+QBYtPbeA7e2j/Nmq7tdv2HKfQr+y+4cV8PklbtaztYDL6yq4wCSvA44mmZ629DM5fJqYBXwrao6ud3uWVX1cJLzgROr6tuz7H8L8N+T7EXzAbAO+JUpr59B8+FwPfCjSVZX1f3ta69KcsuUbd9cVV+f6SBJXgy8A/iJtu4bknyWZl72J9Wd5DnATwMvqKpq/xKRFsQWv1aS17WPm4GbgBfQfBB8GfjJJBckeVVVPdxxf48Dn6MJ+GdU1bZpr58JXFJVPwA+Cbx1ymvTu3pmDP3WK4HLq+q7VfUozV8Wr5ql7odp5qK/KMmbgMdm3as0C4NfK0mAD00J26Oq6qKq+j/ACTRB+sEkv7qAfV4C/C5w6ZMOlPw4zYfK1e1skWewm909081Ud1XtpPmL5jLgjcBVS3lMDYPBr+XsEZrpenf5W+CdSQ4ASHJ4kh9KchjwWFX9Kc3EbyfM8v6ZXA98CPj4tPVnAr+2a7bIqjoMOCyLuy/w9cBpSfZLsj9NV871M9Xd/rc9q6r+Bvj3wLGLOJ4Gzj5+LVtV9UCS/5nkVuDTVfXeJP8c+F/tKMxHgbOAo4ALk/yAZobLX2h3sRG4Ksm3qurEWY5RwExfoJ4BvGHausvb9Tfw1D7+D1bVZbMc46YkHwP+d7vqI1V1c5KfmqHuA4ErkuxL8xfO+TPtU5qLs3NK0sDY1SNJA2PwS9LAGPySNDAGvyQNjMEvSQNj8EvSwBj8kjQw/w8TvwHonTR+KgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Anomaly in  num\n",
      "------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Anomaly in  avg_time\n"
     ]
    }
   ],
   "source": [
    "problems = find_anom(pp.load_esb(data_path))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pp.load_esb(data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df['succee_rate'].values)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
